Data Scientist ( Analytics ) _ D297

Mandatory:

• 4+ years of experience in the field of Data Science and Analytics preferably in CPG or Manufacturing Domain

• Bachelors Degree in Computer Science, Mathematics, Statistics, Economics, Engineering or related field

• Strong programming skills and experience in using Python for Data Science

• Strong analytical techniques, data mining knowledge and proficiency in handling and processing large amounts of data is needed

• Deep Knowledge of statistical and machine learning algorithms for Time Series Forecasting

• Experience in time series forecasting at scale (Like XG Boost, CatBoost, Light GBM , Neural Networks etc)

• Experience in custom EDA (Exploratory Data Analysis) and Output Validations

• Experience in building scalable ML frameworks for demand sensing including Identifying and collecting relevant input data, feature engineering, tuning, and testing.

• Experience in applied analytical methods in the field of Supply chain and planning like demand planning, supply planning, market intelligence, optimal assortments/pricing/inventory etc.

• Strong presentation and communications skills

Nice to have:

• o9 Platform Experience

• Experience with SQL, databases and ETL tools or similar is optional but preferred

• Exposure to distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark or related Bid Data technologies

• Experience in implementing supply chain planning applications will be a plus

What you will do:

• Design and operationalize various kinds of descriptive, predictive and prescriptive analytics and data science relevant in the supply chain planning space.

• Understand business; identify areas of improvement opportunities; collect and analyze data; build models in Python; and present results & insights using the o9 platform.

• Apply a variety of machine learning techniques (clustering, regression, ensemble learning, neural nets, time series etc.) to their real-world advantages/drawbacks

• Develop and deploy models for demand sensing/forecasting, Anomaly detection, Simulation and stochastic models, Market Intelligence etc

• Use latest advancements in AI/ML to solve business problems

• Analyze problems by synthesizing complex information, evaluating alternate methods, and articulating the result with the relevant assumptions/reasons

• Application of common business metrics and the ability to generate new ones as needed